ScholarWorks@성균관대학교

초록

With the proliferation of deep learning technologies across various service domains, the sharing of accelerators such as GPUs, TPUs, and NPUs for inference processing has become increasingly common. These accelerators must efficiently handle multiple deep learning services operating concurrently. However, inference requests, characterized by sequences of short-duration kernels, create significant challenges for online schedulers attempting to maintain Quality of Service (QoS) guarantees. This paper presents QoSlicer, a novel compile-time QoS management framework that employs kernel slicing to relieve the burden on schedulers. By generating multiple pre-determined slicing plans, QoSlicer enables more efficient, lightweight QoS scheduling while ensuring target latency requirements are met. Our approach incorporates a heuristic search algorithm to identify optimal slicing plans and implements robust performance estimation models to validate these plans. Our experimental evaluation across 75 diverse workload combinations demonstrates that QoSlicer improves throughput by an average of 20.2% compared to state-of-the-art scheduling techniques.

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